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https://issues.apache.org/jira/browse/SPARK-7412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-7412:
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Labels: bulk-closed (was: )
> Designing distributed prediction model abstractions for spark.ml
> ----------------------------------------------------------------
>
> Key: SPARK-7412
> URL: https://issues.apache.org/jira/browse/SPARK-7412
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Reporter: Joseph K. Bradley
> Priority: Major
> Labels: bulk-closed
>
> The Pipelines API (spark.ml package) now includes abstractions for
> single-label prediction: Predictor, Classifier, Regressor. These assume
> models are local, where single-Row prediction methods can be used as UDFs.
> We need to think about how to support distributed models in these
> abstractions.
> Should the abstractions be modified somehow? Or should there be parallel (or
> inheriting) abstractions, or a mix-in?
> Motivation: We may start supporting distributed models since linear models,
> random forests, and other models can get large enough to merit distributed
> storage and computation.
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